Rendering 3-D High Dynamic Range Images: Subjective Evaluation of Tone-Mapping Methods and Preferred 3-D Image Attributes
Bibliographic record
Abstract
High dynamic range (HDR) images provide superior picture quality by allowing a larger range of brightness levels to be captured and reproduced than traditional 8-bit low dynamic range (LDR) images. Even with existing 8-bit displays, picture quality can be significantly improved if content is first captured in HDR format, and then is tone-mapped to convert it from HDR to the LDR format. Tone mapping methods have been extensively studied for 2-D images. This paper addresses the problem of presenting stereoscopic tone-mapped HDR images on 3-D LDR displays and how it is different from the 2-D scenario. We first present a subjective psychophysical experiment that evaluates existing tone-mapping operators on 3-D HDR images. The results show that 3-D content derived using tone-mapping is much preferred to that captured directly with a pair of LDR cameras. Global (spatially invariant) and local (spatially variant) tone-mapping methods have similar 3-D effects. The second part of our study focuses on how the preferred level of brightness and the preferred amount of details differ between 3-D and 2-D images by conducting another set of subjective experiments. Our results show that while people selected slightly brighter images in 3-D viewing compared to 2-D, the difference is not statistically significant. However, compared to 2-D images, the subjects consistently preferred having a greater amount of details when watching 3-D. These results suggest that 3-D content should be prepared differently (sharper and possibly slightly brighter) from the same content intended for 2-D displaying, to achieve optimal appearance in each format. The complete database of the original HDR image pairs and their LDR counterparts are available online.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".